Nanotechnology requires highly technological instrumentation to detect and measure on nano scale objects. These objects are often quite delicate and may easily break during measurements and there is a need for solutions in instrumentation that decrease the risk of damaging the sample and at the same time provide fast and reliable measurement solutions.
Scanning tunnelling microscopy (STM) is one of the most important measurement techniques in surface physics and related subjects and has greatly contributed to the major developments in this field in the last 25 years.
The first precursor of the STM was the topografiner, developed by Russell Young, John Ward, and Fredric Scire, which was presented in 1972 in [1]. It was an instrument that could map the topography of a metal surface by scanning a sharp metal tip in a raster at small distance over the surface. Very similar to the STM the tip was moved using piezoelectric drivers and a feedback mechanism. A simple analogue PI-controller held a current between the surface and the tip constant, thus the distance between them. In case of the topografiner the current was a field emission current at higher tip surface distance, in opposite to the tunnelling current in STM. Although Young et al realized the possibility to scan at small distances to the surface in tunnelling mode, too high vibrations and problems with the accuracy of the feedback controller inhibited scanning in this mode. Still it is remarkable how much similarity to the modern STM was present.
Modern STMs generally use logarithmic feedback controllers that are more suitable for the tunnelling regime, but the approach of using an off-the-shelf PI or PID controller is still common. Modern digital controllers allow a wide range of different controller types, but the success of the PID controller has prevented a fundamental change yet. Another opportunity given by the digital approach is the possibility to easily implement complicated algorithms.
A proper tuning of the controller of the STM is quite difficult, especially for the PID controller, as an optimal state in a three dimensional parameter space has to be found (the tuning values for the proportional, the integral and the differential part). Another problem is that the user often has no information about the internal dependence of the parameters and a feeling for the influence of the different parameters has to be earned by a lot of experience with the specific software. An automatic tuning mechanism would simplify this procedure.